At present,the problem of dynamic gesture recognition based on visio is still a difficult point of research.It is difficult to improve the gesture recognition rate in most application backgro unds.The traditional dynamic gesture recognition method is mainly using the smart sensing device and the visual metho d of single or multiple cameras for data acquisition.It is low efficiency and poor accuracy.In recent years,the d eep neural network technology rapids development.Using the advantages of network autonomous learning to extract gest ures relevant features has received extensive attention.For the problem of traditional dynamic gesture recognition low accuracy,this paper builds an Inception-CNN network and LSTM network fusion method.The experimental results on three dynamic gesture da- tasets of Cambridge-Gesture,VIVA and Sheffield Kinect Gesture Dataset (SKIG) s how that the Inception-LSTM cas- cade network has better adaptability.Comparing with existing traditional method s and a variety of popular convolu- tional neural network methods.The gesture average recognition rate and the gest ure recognition rate in each category are higher than the existing methods,fully proving the validity and robustness of the proposed method.